• Janis Grabis Department of Management Information, Riga Technical University (LV)
  • Bohdan Haidabrus Computer Scieince Department, Sumy State University (UA)
  • Serhiy Protsenko Department of Electronics, General and Applied Physics, Sumy State University (UA)
  • Iryna Protsenko Computer Scieince Department, Sumy State University (UA)
  • Anna Rovna Computer Scieince Department, Sumy State University (UA)



Machine Learning, Data Analysis, Project Management, Business Processes


Majority of the IT companies realized that ability to analyse and use data, could be one of the key factors for increasing of number of successful projects, portfolios, programs. Key performance indicators based on data analysis helps organizations be more prosperous in a long term perspective. Also, statistical data are very useful for monitoring and evaluation of project results which are very important for managers, delivery directors, CTO and others high level management of company. The Data Science methods could make more efficient project management in several of business problems. Analysis of historical data from the project life-cycle based on Data Science models could provide more efficient benefits for different stakeholders. Differential of the project data vector with target as an integral evaluation of the project success which allow for the complex correlations between separate features. Therefore, the influence of features importance and override creatures could be decreased on the target. This study propose new approach based on Data Science providing more efficient and accurately project management, taking into account best practices and project performance data.


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How to Cite

J. Grabis, B. Haidabrus, S. Protsenko, I. Protsenko, and A. Rovna, “DATA SCIENCE APPROACH FOR IT PROJECT MANAGEMENT”, ETR, vol. 2, pp. 51–55, Jun. 2019, doi: 10.17770/etr2019vol2.4163.